This document summarizes and analyzes research on analyzing the effectiveness of startup R&D policy. It begins with introducing the necessity of startup R&D policy due to market failures in innovation and entrepreneurship. It then reviews frameworks for entrepreneurship policy and classifications of startup support policies. Several studies on evaluating the effects of startup R&D policies in countries like the US, Israel, and China are summarized. Key challenges in evaluating policy effectiveness like endogeneity, measurement, and long-term effects are also discussed.

1. Event
스타트업 R&D 정책 성과분석 연구의 예시
강석헌 박사과정
런던경영대학원

2. Sukhun Kang
A. 스타트업 R&D 정책의 문제의식, 필요성, 그리고 장단점
B. 스타트업 R&D 정책의 성과 분석
C. 스타트업 R&D 정책 성과 분석 연구의 예시
A. Howell (2017)
B. Chen et al. (2018)
C. Conti (2018)
D. 마무리
2
목차

3. Sukhun Kang
• R&D and Market Failure
• Social returns are greater than private returns (i.e., spillover) – thus
some opportunities won’t be explored due to high-risk and low
profitability (Arrow, 1962; Griliches, 1992;1995).
• Innovation is inherently risky and full of information asymmetry.
• Entrepreneurship and Market Failure
• Early-stage-ideas are even more risky especially if it involves high tech
and innovative ideas – certification (e.g., government subsidy) is
necessary to provide signals for these ideas (Lerner, 1999; Hall, 2000).
• Venture capital is supposed to mediate the risk however they are also
risk-averse thus cannot assume all the risks (Lerner & Kegler, 2000).
• Entrepreneurship perceived as risky career so there is not enough
human capital in the market.
3
스타트업 R&D 정책의 필요성

4. Sukhun Kang 4
Entrepreneurship Policy Framework (Audretsch et al. 2007)

5. Sukhun Kang
• Demand-side (e.g., Incentive)
• R&D 지원 프로그램
• 세금 감면 프로그램 (e.g., 세액 공제)
• 특허 제도 및 보호
• Supply-side (e.g., Human capital, culture, training)
• 대학 연구 지원 제도
• 창업 교육 지원 제도
• R&D Collaboration
• 정부 주도하 대기업-스타트업 간 연구
5
스타트업 지원 정책 분류 (Becker, 2015)

6. Sukhun Kang 6
스타트업 R&D 지원정책의 효과
아이디어
창업
투자
성공
① 창업의 증가
② 첫 투자의 증가
③ 성공 (추가 투자, 엑시트)의 증가
④ 혁신 기업 창업의 증가
⑤ 아이디어의 증가

7. Sukhun Kang
• 정부의 방향성
• 단기적인 효과에 집중: 청년 취업, 예산확보, 성공 확률 높은 기업에 투자집중, 로컬 효과 (Bloom et al., 2002)
• 벤쳐 투자자의 전문성 결여
• 벤쳐 투자자
• 정부 지원금이 들어옴으로써 오는 벤쳐 투자자의 Replacement 효과 (Wallsten, 2000)
• 다음 Stage Funding 혹은 Exit Channel은 존재하는지?
• 인력 자원
• 만약 존재하는 아이디어나 스타트업이 다 별로라면?
• 정부지원금을 받지 못한 스타트업들의 구인난 (Goolsbee, 1998)
• 지원금의 활용도
• 지원 받은 스타트업이 지원금액을 어떻게 활용할 것인가?
• 지원금만을 위한 스타트업은 없는가?
• 지원금을 받음으로써 오는 노력의 감소
• 지원제도가 요하는 추가 노력
• 사업의 본질이 아닌 부수적인 추가 노력을 요함 (각종 보고서 및 문서 처리)
7
스타트업 R&D 지원정책의 단점

8. Sukhun Kang
• Small Business Innovation Research (SBIR) – 미국
• Lerner (1999); Wallsten (2000); Audretsch et al. (2002); Lanahan & Feldman
(2015); Howell (2017); Lanahan & Feldman (2018)
• Innofund – 중국
• Wang et al. (2017)
• TIP – 이스라엘
• Lach (2002); Conti (2018)
• Tekes – 핀란드
• Einio (2014); Autio & Rannikko (2015)
• Innovation Investment Fund – 영국
• Mikromezzaninfonds / ZIM – 독일
• Skolkovo Foundation – 러시아
• InnovaChile - 칠레
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해외 스타트업 지원 정책

9. Sukhun Kang 9
팁스란?

10. Sukhun Kang 10
팁스의 문제의식
예시: 팁스
투자
팁스
성공
창업
아이디어
(4)
(2)
(3)
(1)

11. Sukhun Kang
• David et al. (2000)
• Becker (2015)
• Hall & Van Reenen (2000)
• Zuniga-Vicente et al. (2014)
• Garcia-Quevedo (2004)
• Klette et al. (2000)
• Dimos & Pugh (2016)
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정부 R&D 지원 정책 Review 논문

12. Sukhun Kang
• Endogeneity (i.e., reverse causality, selection bias, and omitted
variable bias)
• Wallsten (2000); Hyytinen & Toivanen (2005)
• Only those who will be successful will be applying for grants
• Only those with high risk will be applying for grants
• i.e., Those that get subsidies and do not get them are inherently different
• Measurement
• R&D Output
• Is patent a good measure of innovation (or performance)?
• Spillover
• What about non-recipients?
• Transformational impact? Any other indirect effect?
• Long-term effect
12
정부 지원 정책 성과 분석의 어려움

13. Sukhun Kang
• Regression with controls
• Chen et al. (2018)
• Matching
• Conti (2018)
• Fixed effects or DiD
• Conti (2018)
• Selection model
• Conti (2018)
• Regression Discontinuity Design
• Howell (2017)
• Randomized Controlled Trial (i.e., Experiment)
• 정책 디자인 단계부터 성과 분석을 미리 고려함으로써 정책 분석에
도움이 될수있음.
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지원 정책 성과 분석 방법론 (Jaffe, 2002)

14. Sukhun Kang
• Selection Effect
• Only high-quality incubators apply and receive “partner” status
• Only high-quality startups apply and receive financing
• Treatment Effect
• Less Equity: More Incentives
• Value-Add: Networking & Business Advice
• Financial Constraint: Prototyping?
• Certification Effect
• Underlying quality does not matter
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3 Types of Effects
지원제도 성과 분석

15. Sukhun Kang
• A. Does it work?
• Causal Inference (Selection, Treatment, Certification)
• Selection & Treatment (Howell, 2017)
• Selection (Conti, 2018)
• Certification (Chen et al. 2018)
• B. How does it work (if there is an effect)?
• Theory & Mechanisms
• C. How can it be made to work?
• Practical Implication
15
세가지 질문
스타트업 R&D 지원정책 성과 분석

16. Event
"Financing Innovation: Evidence from R&D
Grants”
Sabrina Howell (2017)
American Economic Review

17. Sukhun Kang
• RQ: How does a government grant that subsidizes R&D in new
ventures affect innovative, financial, and commercial success?
• Setting: U.S. SBIR Program
• 7,436 small high-tech firms and over $884 million from 1983 to 2013
• Key Findings
• Improves subsequent venture capital funding (10% -> 19%)
• Improves patenting (30%) and revenue (30%)
• These effects are stronger for H/W startups and young firms
• Mechanism: Prototyping (basic research on a new technology or
testing and demonstrating an existing technology)
• Compared to Phase 1, Phase 2 Grant has a little or no effect
17
Overview
Howell (2017)

18. Sukhun Kang
• Setting: U.S. SBIR Program
• $2.2 billion every year
• 2.7% (3.2%) of extramural R&D budget of 11 Federal Agenciesto SBIR
program
• Phase 1: $150,000 for 9 months
• Phase 2: $1 million after Phase 1
• Applicants proposes where to use the grant, but it is not enforced
• DOE officials evaluate each application based on
• Strength of the scientific / technical approach
• Ability to carry out the project
• Commercialization effect
• Data
• All applications received at DOE Fossil Energy and Renewable Energy
Department
• $884 million between 1983 and 2013
18
Setting & Data
Howell (2017)

19. Sukhun Kang
• Method: Regression Discontinuity (RD) using a rank based on a
grant application
• Ranks provide a natural experiment by providing a valid
discontinuity cutoff point
• All applicants are likely to put similar amount of efforts in preparing
applicants
• The program official should not manipulate applicants around the cutoff
point – When they rank the applicants they don’t know where the cutoff
is.
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Empirical Strategy
Howell (2017)

20. Sukhun Kang
• Innovation
• Citation weighted patents
• Finance
• Venture Capital
• Revenue
• Successful Exits and Survival
• IPO or Acquisitions
20
Phase 1 Grant Impact on Firm Outcomes (1)
A. Does it work? Howell (2017)

21. Sukhun Kang 21
Phase 1 Grant Impact on Firm Outcomes (2)
A. Does it work? Howell (2017)

22. Sukhun Kang 22
Phase 1 Grant Impact on Firm Outcomes (3)
A. Does it work? Howell (2017)

23. Sukhun Kang
• Financial Constraint
• Firms without a patent vs. with patent(s)
• Hardware vs. Software
• Young vs. Old
• Emerging sector vs. Mature sector
• Certification
• Signaling
• Funding
• Equity
• Incentives
• Prototyping
23
Phase 1 Grant Mechanisms
B. How does it work? Howell (2017)

24. Sukhun Kang 24
The Grant Mechanisms: Financing Constraint
B. How does it work? Howell (2017)

25. Sukhun Kang 25
The Grant Mechanisms: Certification
B. How does it work? Howell (2017)

26. Sukhun Kang 26
The Grant Mechanisms: Prototyping
B. How does it work? Howell (2017)

27. Sukhun Kang
• Should invest in a large number of small and early-stage
startups
• Should invest more in younger and first-time applicants
• Caveats
• US VC industry vs. Other countries’ VC industries
• US has an active startup exit channel (e.g., IPO, subsequent funding,
M&A)
• Deep technology (i.e., energy industry)
27
Policy Implication
C. How can it be made to work? Howell (2017)

28. Event
“The Distinct Signalling Effects of R&D Subsidy
and non-R&D subsidy on IPO Performance on IT
Entrepreneurial Firms in China”
Jin Chen, Chng Suang Heng, Bernard C.Y.
Tan & Zhijie Lin (2018)
Research Policy

29. Sukhun Kang
• RQ: How do R&D subsidy and non-R&D subsidy differ in their
effects on entrepreneurial performance in China?
• Setting: China’s IPO Market
• 269 IPO in IT industry from 2004 to 2015
• Key Findings
• R&D subsidy and IPO performance has an inverted U-shape
relationship while non-R&D subsidy has a positive effect on IPO
performance.
• Government ownership & patent intensity flatten the inverted U-shape
relationship between R&D subsidy and IPO performance while it has
no effect on non-R&D subsidy.
29
Overview
Chen et al. (2018)

30. Sukhun Kang
• R&D Subsidy and IPO Performance
• R&D Subsidy aims to solve the market failure (spillover, information
asymmetry)
• However, it also conveys the risks associated with the company.
• Non-R&D Subsidy and IPO Performance
• Definition: Marketing, Technology purchase, Export, Employment,
Training, etc.
• It conveys signals to external parties about the firm’s ability to conduct
non-R&D activities.
30
Theoretical Framework
Chen et al. (2018)

31. Sukhun Kang
• Sample: 269 IT firms that went public between 2004 and 2015
• Dependent Variable
• IPO Performance measured by 1) net proceeds 2) pre-market valuation
• Independent Variable
• Total amount of R&D subsidy (avg = 5.9, min = 0, max = 121.90) and
non-R&D subsidy (avg = 4.14, min = 0, max = 73.04)
• Based on the title or goal of the subsidy
31
Setting & Data
Chen et al. (2018)

32. Sukhun Kang 32
The effect of subsidies on IPO Performance
1. Does it work? Chen et al. (2018)

33. Sukhun Kang 33
The effect of subsidies on IPO Performance – Moderating Variables
1. Does it work? & 2. How does it work? Chen et al. (2018)

34. Sukhun Kang
• Propensity Score Matching
• State ownership, political tie, patent intensity, VC endorsement dummy,
location dummy, software dummy, Hi Tech Park dummy
• Instrument Variable
• A region-based pooled R&D subsidy and non R&D subsidy availability
• Policy change
• Implementation of Medium-term to Long-term Plan for Science and
Technology Development
• Limit to firms who received subsidy after the reform
34
Robustness Checks
1. Does it work? Chen et al. (2018)

35. Sukhun Kang
• Understanding of symbolic role of government subsidy.
• Are we looking for treatment effect or symbolic (certification) effect?
• R&D and non-R&D subsidies may signal different aspects of the
recipients.
• Who will benefit from R&D subsidy and who won’t?
• Vice versa for non-R&D subsidy
• Caveats
• One industry and one country
35
Policy Implication
C. How can it be made to work? Chen et al. (2018)

36. Event
“Entrepreneurial Finance and the Effects of
Restrictions on Government R&D Subsidies”
Annamaria Conti (2018)
Organization Science

37. Sukhun Kang
• RQ: How does the level of restrictions on the external transfer of
knowhow affect the startup’s decision to apply for subsidy? And, how
does it influence the venture outcomes?
• Setting: Israel OCS Subsidy
• 2,304 small high-tech startups (in which 907 applied for an OCS Subsidy)
• Key Findings
• Before the restriction removal, startups with private financing were less likely
to apply for a subsidy.
• After the reform, startups with private financing were more likely to apply
(14%).
• After the reform, the subsidy recipients were more likely to survive (~20%),
more likely to receiving external investment (~25%), and more likely to patent
(~28%).
37
Overview
Conti (2018)

38. Sukhun Kang 38
Effect of Israeli Policy Reform on Startups’ Decision to Apply
1. Does it work? Conti (2018)

39. Sukhun Kang 39
Effect of Israeli Policy Reform on Venture Outcomes
1. Does it work? Conti (2018)

40. Sukhun Kang 40
Policy Reform Effect
B. How does it work? Conti (2018)
• Before the reform, there was no significance difference in performance
between subsidized and nonsubsidized applicants.
• However, after the reform, the probability of a startups surviving, its
likelihood of attracting VC, and its innovation output increased only for
subsidized ones.
• The policy must have increased the startups’ marginal value from exerting
effort in a subsidized project, and concurrently effort.

41. Sukhun Kang
• Should remove restrictions where possible.
• Especially for small economies like Israel (and South Korea)
• E.g., Maybe tax incentives for keeping the R&D facility local would be more
effective than posing restrictions on the external transfer of subsidized know-how
• Should probably invest in innovation capacity of local
universities and large firms in parallel.
• Entrepreneurs should carefully examine the restrictions of
subsidy they apply to.
• Caveats
• Policy restriction removal can be endogenous to the Israel’s setting
41
Policy Implication
C. How can it be made to work? Conti (2018)

42. Event
감사합니다!
강석헌
skang@london.edu